Open AccessARTIFICIAL INTELLIGENCEINTERVENTION TO URBAN BUILDING RENEWABLE ENERGY MODELING INTERVENTION FOR ROBUST FLEXIBLE COMMUNITIES
Author(s) -
Sammar Z. Allam
Publication year - 2021
Publication title -
international journal of advanced research
Language(s) - English
Resource type - Journals
ISSN - 2320-5407
DOI - 10.21474/ijar01/12586
Subject(s) - renewable energy , energy engineering , energy consumption , computer science , electricity generation , energy management , distributed generation , architectural engineering , environmental economics , energy (signal processing) , engineering , civil engineering , power (physics) , electrical engineering , statistics , physics , mathematics , quantum mechanics , economics
This research coveys a comparative analysis between Urban Building energy model (UBEM) generated by scholar, researchers, and professional in academia and industry while highlighting the reliable main components to manifest a successful and reliable UBEM technologies. Nevertheless, it consolidates distributed generation on building blocks rather than a whole district relying on renewable energy sources. It guides engineers through energy system model simulation on Openmodelica platform to feed green sustained communities. Moreover, energy use-pattern is mapped and analyzed by internet of things (IOT) technologies to fine-tune energy uses and refine use-pattern. Demonstrating artificial Intelligence (AI) algorithmto predict energy consumption can reflect on the amount of energy required for storage to cover energy needs. AI shapes a robust positive energy district (PED) through storinggenerated renewable solar or bio-energy to cover predicted energy use-pattern.Distributed -power-plant stations capacity to cover clusters using AI in predicting energy consumption consolidates on-site energy generation recommended by multiple International rating systems. AI-based Energy management plan guide engineers and planners to design distributed-power-plants of energy generation capacity lies between the actual energy need and a predicted scenario.